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Artificial Bee Colony-Based Dynamic Sliding Mode Controller for Integrating Processes with Inverse Response and Deadtime

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Applications of Computational Intelligence (ColCACI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1746))

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Abstract

A strategy for optimizing the settings of a dynamical sliding mode controller using an artificial bee colony optimization algorithm is proposed in this paper. The performance of the obtained controller is then evaluated and compared to that of a conventional PID and a dynamical sliding mode controller that has been optimized through a heuristics-based strategy by simulating two integrating linear systems with dead time and inverse response. By utilizing the suggested strategy, it was possible to increase both performance indices and transient characteristics.

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References

  1. Artificial Bee Colony (ABC) algorithm. Intelligent Systems Research Group Department of Computer Engineering at Erciyes University (2009). https://abc.erciyes.edu.tr/

  2. Abachizadeh, M., Yazdi, M.R.H., Yousefi-Koma, A.: Optimal tuning of PID controllers using artificial bee colony algorithm, pp. 379–384 (2010). https://doi.org/10.1109/AIM.2010.5695861

  3. Asimbaya, E., Cabrera, H., Camacho, O., Chávez, D., Leica, P.: A dynamical discontinuous control approach for inverse response chemical processes. In: 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC), pp. 1–6. IEEE (2017)

    Google Scholar 

  4. Baez, E., Bravo, Y., Chavez, D., Camacho, O.: Tuning parameters optimization approach for dynamical sliding mode controllers. IFAC-PapersOnLine 51(13), 656–661 (2018)

    Article  Google Scholar 

  5. Báez, E., Bravo, Y., Leica, P., Chávez, D., Camacho, O.: Dynamical sliding mode control for nonlinear systems with variable delay. In: 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC), pp. 1–6. IEEE (2017)

    Google Scholar 

  6. Burnham, K., Zinober, A., Koshkouei, A.: Dynamic sliding mode control design. IEE Proc. - Control Theor. Appl. 152(4), 392–396 (2005). https://doi.org/10.1049/ip-cta:20055133

    Article  Google Scholar 

  7. Camacho, O., Smith, C.A.: Sliding mode control: an approach to regulate nonlinear chemical processes. ISA Trans. 39(2), 205–218 (2000). https://doi.org/10.1016/S0019-0578(99)00043-9

    Article  Google Scholar 

  8. De Battista, H., Mantz, R.J., Christiansen, C.F.: Dynamical sliding mode power control of wind driven induction generators. IEEE Trans. Energy Convers. 15(4), 451–457 (2000)

    Article  Google Scholar 

  9. De Castro, L., José, F., von Zuben, A.A.: Artificial immune systems: Part I-basic theory and applications (2000)

    Google Scholar 

  10. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Comput. Intell. Mag. 1(4), 28–39 (2006). https://doi.org/10.1109/MCI.2006.329691

    Article  Google Scholar 

  11. Espín, J., Camacho, O.: A proposal of dynamic sliding mode controller for integrating processes with inverse response and deadtime. In: 2021 IEEE Fifth Ecuador Technical Chapters Meeting (ETCM), pp. 1–6. IEEE (2021)

    Google Scholar 

  12. Ghanem, W., Jantan, A.: Using hybrid artificial bee colony algorithm and particle swarm optimization for training feed-forward neural networks. J. Theor. Appl. Inf. Technol. 67, 664–674 (2014)

    Google Scholar 

  13. Herrera, M., Camacho, O., Leiva, H., Smith, C.: An approach of dynamic sliding mode control for chemical processes. J. Process Control 85, 112–120 (2020)

    Article  Google Scholar 

  14. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975). Second edition, 1992

    Google Scholar 

  15. Iinoya, K., Altpeter, R.J.: Inverse response in process control. Ind. Eng. Chem. 54(7), 39–43 (1962)

    Article  Google Scholar 

  16. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report - tr06. Erciyes University (2005)

    Google Scholar 

  17. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39, 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  18. Kaya, Ibrahim: Controller design for integrating processes with inverse response and dead time based on standard forms. Electr. Eng. 100(3), 2011–2022 (2018). https://doi.org/10.1007/s00202-018-0679-7

    Article  Google Scholar 

  19. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995). https://doi.org/10.1109/ICNN.1995.488968

  20. Korupu, V.L., Muthukumarasamy, M.: A comparative study of various smith predictor configurations for industrial delay processes. Chem. Prod. Process Model. 17(6), 701–732 (2021)

    Article  Google Scholar 

  21. Kunusch, C., Puleston, P., Mayosky, M.: Fundamentals of sliding-mode control design. In: Kunusch, C., Puleston, P., Mayosky, M. (eds.) Sliding-Mode Control of PEM Fuel Cells. Advances in Industrial Control, pp. 35–71. Springer, London (2012). https://doi.org/10.1007/978-1-4471-2431-3_3

    Chapter  Google Scholar 

  22. Lin, F.J., Chen, S.Y., Shyu, K.K.: Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system. IEEE Trans. Neural Netw. 20(6), 938–951 (2009). https://doi.org/10.1109/TNN.2009.2014228

    Article  Google Scholar 

  23. Luyben, W.L.: Identification and tuning of integrating processes with deadtime and inverse response. Ind. Eng. Chem. Res. 42(13), 3030–3035 (2003)

    Article  Google Scholar 

  24. Mehta, U., Rojas, R.: Smith predictor based sliding mode control for a class of unstable processes. Trans. Inst. Meas. Control. 39(5), 706–714 (2017)

    Article  Google Scholar 

  25. Mohammad, A., Ehsan, S.S.: Sliding mode PID-controller design for robot manipulators by using fuzzy tuning approach. In: 2008 27th Chinese Control Conference, pp. 170–174. IEEE (2008)

    Google Scholar 

  26. Moin, N., Zinober, A., Harley, P.: Sliding mode control design using genetic algorithms. In: First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 238–244. IET (1995)

    Google Scholar 

  27. Pai, N.S., Chang, S.C., Huang, C.T.: Tuning PI/PID controllers for integrating processes with deadtime and inverse response by simple calculations. J. Process Control 20(6), 726–733 (2010)

    Article  Google Scholar 

  28. Piltan, F., Boroomand, B., Jahed, A., Rezaie, H.: Methodology of mathematical error-based tuning sliding mode controller. Int. J. Eng. 6(2), 96–117 (2012)

    Google Scholar 

  29. Proaño, P., Capito, L., Rosales, A., Camacho, O.: A dynamical sliding mode control approach for long deadtime systems. In: 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT), pp. 0108–0113. IEEE (2017)

    Google Scholar 

  30. Rajabioun, R.: Cuckoo optimization algorithm. Appl. Soft Comput. 11(8), 5508–5518 (2011)

    Article  Google Scholar 

  31. Rojas, R., Camacho, O., González, L.: A sliding mode control proposal for open-loop unstable processes. ISA Trans. 43(2), 243–255 (2004)

    Article  Google Scholar 

  32. Sira-Ramírez, H.: Dynamical sliding mode control strategies in the regulation of nonlinear chemical processes. Int. J. Control 56(1), 1–21 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  33. Sira-Ramirez, H., Llanes-Santiago, O.: Dynamical discontinuous feedback strategies in the regulation of nonlinear chemical processes. IEEE Trans. Control Syst. Technol. 2(1), 11–21 (1994)

    Article  Google Scholar 

  34. Utkin, V.: Variable structure systems with sliding modes. IEEE Trans. Autom. Control 22(2), 212–222 (1977). https://doi.org/10.1109/TAC.1977.1101446

    Article  MathSciNet  MATH  Google Scholar 

  35. Utkin, V., Lee, H.: Chattering problem in sliding mode control systems. In: 2006 International Workshop on Variable Structure Systems, VSS 2006, pp. 346–350. IEEE (2006). https://doi.org/10.1109/VSS.2006.1644542

  36. Utkin, Vadim, Poznyak, Alex, Orlov, Yury V.., Polyakov, Andrey: Road Map for Sliding Mode Control Design. SpringerBriefs in Mathematics, Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41709-3

    Book  MATH  Google Scholar 

  37. Young, K., Utkin, V., Ozguner, U.: A control engineer’s guide to sliding mode control. IEEE Trans. Control Syst. Technol. 7(3), 328–342 (1999). https://doi.org/10.1109/87.761053, http://ieeexplore.ieee.org/document/761053/

  38. Yuan, Z., Montes de Oca, M., Birattari, M., Stützle, T.: Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms. Swarm Intell. 6, 49–75 (2012). https://doi.org/10.1007/s11721-011-0065-9

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Acknowledgment

J. Espin and S. Estrada thank the Advanced Control Systems Research Group at USFQ for the research internship.

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Authors and Affiliations

  1. Department of Electronics Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Jorge Espin & Sebastian Estrada

  2. Colegio de Ciencias e Ingenierías “El Politécnico”, Universidad San Francisco de Quito USFQ, Quito, 170157, Ecuador

    Jorge Espin, Sebastian Estrada, Diego S. Benítez & Oscar Camacho

Authors
  1. Jorge Espin
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  2. Sebastian Estrada
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  3. Diego S. Benítez
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  4. Oscar Camacho
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Corresponding author

Correspondence to Diego S. Benítez .

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Editors and Affiliations

  1. Universidad del Rosario, Bogotá, Colombia

    Alvaro David Orjuela-Cañón

  2. Universidad Autónoma de Occidente, Cali, Colombia

    Jesus Lopez

  3. Universidad Politécnica de Madrid, Madrid, Spain

    Julian David Arias-Londoño

  4. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Espin, J., Estrada, S., Benítez, D.S., Camacho, O. (2023). Artificial Bee Colony-Based Dynamic Sliding Mode Controller for Integrating Processes with Inverse Response and Deadtime. In: Orjuela-Cañón, A.D., Lopez, J., Arias-Londoño, J.D., Figueroa-García, J.C. (eds) Applications of Computational Intelligence. ColCACI 2022. Communications in Computer and Information Science, vol 1746. Springer, Cham. https://doi.org/10.1007/978-3-031-29783-0_4

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  • DOI: https://doi.org/10.1007/978-3-031-29783-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29782-3

  • Online ISBN: 978-3-031-29783-0

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